






'''simple docstring'''







import itertools
from dataclasses import dataclass
from typing import Any, Callable, Dict, List, Optional, Union

import pandas as pd
import pyarrow as pa

import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table import table_cast
from datasets.utils.py_utils import Literal


UpperCamelCase     :   int	      =		datasets.utils.logging.get_logger(__name__)

UpperCamelCase     :   List[str]	      =		['names', 'prefix']
UpperCamelCase     :   List[str]	      =		['warn_bad_lines', 'error_bad_lines', 'mangle_dupe_cols']
UpperCamelCase     :   Optional[int]	      =		['encoding_errors', 'on_bad_lines']
UpperCamelCase     :   Optional[int]	      =		['date_format']




@dataclass
class 		UpperCamelCase__     (datasets.BuilderConfig      ):




		'''simple docstring'''





		_UpperCamelCase						=       ","
		_UpperCamelCase						=       None
		_UpperCamelCase						=       "infer"
		_UpperCamelCase						=       None
		_UpperCamelCase						=       None
		_UpperCamelCase						=       None
		_UpperCamelCase						=       None
		_UpperCamelCase						=       None
		_UpperCamelCase						=       True
		_UpperCamelCase						=       None
		_UpperCamelCase						=       None
		_UpperCamelCase						=       None
		_UpperCamelCase						=       None
		_UpperCamelCase						=       False
		_UpperCamelCase						=       None
		_UpperCamelCase						=       None
		_UpperCamelCase						=       None
		_UpperCamelCase						=       True
		_UpperCamelCase						=       True
		_UpperCamelCase						=       False
		_UpperCamelCase						=       True
		_UpperCamelCase						=       None
		_UpperCamelCase						=       "."
		_UpperCamelCase						=       None
		_UpperCamelCase						=       '"'
		_UpperCamelCase						=       0
		_UpperCamelCase						=       None
		_UpperCamelCase						=       None
		_UpperCamelCase						=       None
		_UpperCamelCase						=       None
		_UpperCamelCase						=       True
		_UpperCamelCase						=       True
		_UpperCamelCase						=       0
		_UpperCamelCase						=       True
		_UpperCamelCase						=       False
		_UpperCamelCase						=       None
		_UpperCamelCase						=       10000
		_UpperCamelCase						=       None
		_UpperCamelCase						=       "strict"
		_UpperCamelCase						=       "error"
		_UpperCamelCase						=       None






		def       UpperCamelCase_       (   self							):
								if self.delimiter is not None:
														lowerCamelCase__								=	self.delimiter
								if self.column_names is not None:
														lowerCamelCase__								=	self.column_names






		@property
		def       UpperCamelCase_       (   self							):
								lowerCamelCase__								=	{
								    """sep""": self.sep,
								    """header""": self.header,
								    """names""": self.names,
								    """index_col""": self.index_col,
								    """usecols""": self.usecols,
								    """prefix""": self.prefix,
								    """mangle_dupe_cols""": self.mangle_dupe_cols,
								    """engine""": self.engine,
								    """converters""": self.converters,
								    """true_values""": self.true_values,
								    """false_values""": self.false_values,
								    """skipinitialspace""": self.skipinitialspace,
								    """skiprows""": self.skiprows,
								    """nrows""": self.nrows,
								    """na_values""": self.na_values,
								    """keep_default_na""": self.keep_default_na,
								    """na_filter""": self.na_filter,
								    """verbose""": self.verbose,
								    """skip_blank_lines""": self.skip_blank_lines,
								    """thousands""": self.thousands,
								    """decimal""": self.decimal,
								    """lineterminator""": self.lineterminator,
								    """quotechar""": self.quotechar,
								    """quoting""": self.quoting,
								    """escapechar""": self.escapechar,
								    """comment""": self.comment,
								    """encoding""": self.encoding,
								    """dialect""": self.dialect,
								    """error_bad_lines""": self.error_bad_lines,
								    """warn_bad_lines""": self.warn_bad_lines,
								    """skipfooter""": self.skipfooter,
								    """doublequote""": self.doublequote,
								    """memory_map""": self.memory_map,
								    """float_precision""": self.float_precision,
								    """chunksize""": self.chunksize,
								    """encoding_errors""": self.encoding_errors,
								    """on_bad_lines""": self.on_bad_lines,
								    """date_format""": self.date_format,
								}

								# some kwargs must not be passed if they don't have a default value
								# some others are deprecated and we can also not pass them if they are the default value
								for pd_read_csv_parameter in _PANDAS_READ_CSV_NO_DEFAULT_PARAMETERS + _PANDAS_READ_CSV_DEPRECATED_PARAMETERS:
														if pd_read_csv_kwargs[pd_read_csv_parameter] == getattr(CsvConfig() ,_lowerCAmelCase							):
																				del pd_read_csv_kwargs[pd_read_csv_parameter]

        # Remove 2.0 new arguments
								if not (datasets.config.PANDAS_VERSION.major >= 2):
														for pd_read_csv_parameter in _PANDAS_READ_CSV_NEW_2_0_0_PARAMETERS:
																				del pd_read_csv_kwargs[pd_read_csv_parameter]

        # Remove 1.3 new arguments
								if not (datasets.config.PANDAS_VERSION.major >= 1 and datasets.config.PANDAS_VERSION.minor >= 3):
														for pd_read_csv_parameter in _PANDAS_READ_CSV_NEW_1_3_0_PARAMETERS:
																				del pd_read_csv_kwargs[pd_read_csv_parameter]

								return pd_read_csv_kwargs




class 		UpperCamelCase__     (datasets.ArrowBasedBuilder      ):




		'''simple docstring'''





		_UpperCamelCase						=       CsvConfig






		def       UpperCamelCase_       (   self							):
								return datasets.DatasetInfo(features=self.config.features							)






		def       UpperCamelCase_       (   self ,_lowerCAmelCase							):
								if not self.config.data_files:
														raise ValueError(F'''At least one data file must be specified, but got data_files={self.config.data_files}'''							)
								lowerCamelCase__								=	dl_manager.download_and_extract(self.config.data_files							)
								if isinstance(_lowerCAmelCase ,(str, list, tuple)							):
														lowerCamelCase__								=	data_files
														if isinstance(_lowerCAmelCase ,_lowerCAmelCase							):
																				lowerCamelCase__								=	[files]
														lowerCamelCase__								=	[dl_manager.iter_files(_lowerCAmelCase							) for file in files]
														return [datasets.SplitGenerator(name=datasets.Split.TRAIN ,gen_kwargs={"""files""": files}							)]
								lowerCamelCase__								=	[]
								for split_name, files in data_files.items():
														if isinstance(_lowerCAmelCase ,_lowerCAmelCase							):
																				lowerCamelCase__								=	[files]
														lowerCamelCase__								=	[dl_manager.iter_files(_lowerCAmelCase							) for file in files]
														splits.append(datasets.SplitGenerator(name=_lowerCAmelCase ,gen_kwargs={"""files""": files}							)							)
								return splits






		def       UpperCamelCase_       (   self ,_lowerCAmelCase							):
								if self.config.features is not None:
														lowerCamelCase__								=	self.config.features.arrow_schema
														if all(not require_storage_cast(_lowerCAmelCase							) for feature in self.config.features.values()							):
																				# cheaper cast
																				lowerCamelCase__								=	pa.Table.from_arrays([pa_table[field.name] for field in schema] ,schema=_lowerCAmelCase							)
														else:
																				# more expensive cast; allows str <-> int/float or str to Audio for example
																				lowerCamelCase__								=	table_cast(_lowerCAmelCase ,_lowerCAmelCase							)
								return pa_table






		def       UpperCamelCase_       (   self ,_lowerCAmelCase							):
								lowerCamelCase__								=	self.config.features.arrow_schema if self.config.features else None
								# dtype allows reading an int column as str
								lowerCamelCase__								=	(
								    {
								        name: dtype.to_pandas_dtype() if not require_storage_cast(_lowerCAmelCase							) else object
								        for name, dtype, feature in zip(schema.names ,schema.types ,self.config.features.values()							)
								    }
								    if schema is not None
								    else None
								)
								for file_idx, file in enumerate(itertools.chain.from_iterable(_lowerCAmelCase							)							):
														lowerCamelCase__								=	pd.read_csv(_lowerCAmelCase ,iterator=_lowerCAmelCase ,dtype=_lowerCAmelCase ,**self.config.pd_read_csv_kwargs							)
														try:
																				for batch_idx, df in enumerate(_lowerCAmelCase							):
																										lowerCamelCase__								=	pa.Table.from_pandas(_lowerCAmelCase							)
																										# Uncomment for debugging (will print the Arrow table size and elements)
																										# logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
																										# logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
																										yield (file_idx, batch_idx), self._cast_table(_lowerCAmelCase							)
														except ValueError as e:
																				logger.error(F'''Failed to read file \'{file}\' with error {type(_lowerCAmelCase							)}: {e}'''							)
																				raise



"""simple docstring"""



import argparse

import requests
import torch
from PIL import Image
from torchvision.transforms import Compose, Normalize, Resize, ToTensor

from transformers import SwinaSRConfig, SwinaSRForImageSuperResolution, SwinaSRImageProcessor


def 			_UpperCamelCase						(    _A   )     ->	str:





    """simple docstring"""

    _UpperCAmelCase        = SwinaSRConfig()

    if "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
        _UpperCAmelCase        = 4
    elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
        _UpperCAmelCase        = 4
        _UpperCAmelCase        = 4_8
        _UpperCAmelCase        = """pixelshuffle_aux"""
    elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
        _UpperCAmelCase        = [6, 6, 6, 6]
        _UpperCAmelCase        = 6_0
        _UpperCAmelCase        = [6, 6, 6, 6]
        _UpperCAmelCase        = """pixelshuffledirect"""
    elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
        _UpperCAmelCase        = 4
        _UpperCAmelCase        = """nearest+conv"""
    elif "Swin2SR_Jpeg_dynamic" in checkpoint_url:
        _UpperCAmelCase        = 1
        _UpperCAmelCase        = 1
        _UpperCAmelCase        = 1_2_6
        _UpperCAmelCase        = 7
        _UpperCAmelCase        = 255.0
        _UpperCAmelCase        = """"""

    return config


def 			_UpperCamelCase						(    _A       ,   _A   )     ->	Tuple:





    """simple docstring"""

    if "patch_embed.proj" in name and "layers" not in name:
        _UpperCAmelCase        = name.replace("""patch_embed.proj"""       ,   """embeddings.patch_embeddings.projection"""   )
    if "patch_embed.norm" in name:
        _UpperCAmelCase        = name.replace("""patch_embed.norm"""       ,   """embeddings.patch_embeddings.layernorm"""   )
    if "layers" in name:
        _UpperCAmelCase        = name.replace("""layers"""       ,   """encoder.stages"""   )
    if "residual_group.blocks" in name:
        _UpperCAmelCase        = name.replace("""residual_group.blocks"""       ,   """layers"""   )
    if "attn.proj" in name:
        _UpperCAmelCase        = name.replace("""attn.proj"""       ,   """attention.output.dense"""   )
    if "attn" in name:
        _UpperCAmelCase        = name.replace("""attn"""       ,   """attention.self"""   )
    if "norm1" in name:
        _UpperCAmelCase        = name.replace("""norm1"""       ,   """layernorm_before"""   )
    if "norm2" in name:
        _UpperCAmelCase        = name.replace("""norm2"""       ,   """layernorm_after"""   )
    if "mlp.fc1" in name:
        _UpperCAmelCase        = name.replace("""mlp.fc1"""       ,   """intermediate.dense"""   )
    if "mlp.fc2" in name:
        _UpperCAmelCase        = name.replace("""mlp.fc2"""       ,   """output.dense"""   )
    if "q_bias" in name:
        _UpperCAmelCase        = name.replace("""q_bias"""       ,   """query.bias"""   )
    if "k_bias" in name:
        _UpperCAmelCase        = name.replace("""k_bias"""       ,   """key.bias"""   )
    if "v_bias" in name:
        _UpperCAmelCase        = name.replace("""v_bias"""       ,   """value.bias"""   )
    if "cpb_mlp" in name:
        _UpperCAmelCase        = name.replace("""cpb_mlp"""       ,   """continuous_position_bias_mlp"""   )
    if "patch_embed.proj" in name:
        _UpperCAmelCase        = name.replace("""patch_embed.proj"""       ,   """patch_embed.projection"""   )

    if name == "norm.weight":
        _UpperCAmelCase        = """layernorm.weight"""
    if name == "norm.bias":
        _UpperCAmelCase        = """layernorm.bias"""

    if "conv_first" in name:
        _UpperCAmelCase        = name.replace("""conv_first"""       ,   """first_convolution"""   )

    if (
        "upsample" in name
        or "conv_before_upsample" in name
        or "conv_bicubic" in name
        or "conv_up" in name
        or "conv_hr" in name
        or "conv_last" in name
        or "aux" in name
    ):
        # heads
        if "conv_last" in name:
            _UpperCAmelCase        = name.replace("""conv_last"""       ,   """final_convolution"""   )
        if config.upsampler in ["pixelshuffle", "pixelshuffle_aux", "nearest+conv"]:
            if "conv_before_upsample.0" in name:
                _UpperCAmelCase        = name.replace("""conv_before_upsample.0"""       ,   """conv_before_upsample"""   )
            if "upsample.0" in name:
                _UpperCAmelCase        = name.replace("""upsample.0"""       ,   """upsample.convolution_0"""   )
            if "upsample.2" in name:
                _UpperCAmelCase        = name.replace("""upsample.2"""       ,   """upsample.convolution_1"""   )
            _UpperCAmelCase        = """upsample.""" + name
        elif config.upsampler == "pixelshuffledirect":
            _UpperCAmelCase        = name.replace("""upsample.0.weight"""       ,   """upsample.conv.weight"""   )
            _UpperCAmelCase        = name.replace("""upsample.0.bias"""       ,   """upsample.conv.bias"""   )
        else:
            pass
    else:
        _UpperCAmelCase        = """swin2sr.""" + name

    return name


def 			_UpperCamelCase						(    _A       ,   _A   )     ->	int:





    """simple docstring"""

    for key in orig_state_dict.copy().keys():
        _UpperCAmelCase        = orig_state_dict.pop(_A   )

        if "qkv" in key:
            _UpperCAmelCase        = key.split("""."""   )
            _UpperCAmelCase        = int(key_split[1]   )
            _UpperCAmelCase        = int(key_split[4]   )
            _UpperCAmelCase        = config.embed_dim

            if "weight" in key:
                _UpperCAmelCase        = val[:dim, :]
                _UpperCAmelCase        = val[dim : dim * 2, :]
                _UpperCAmelCase        = val[-dim:, :]
            else:
                _UpperCAmelCase        = val[:dim]
                _UpperCAmelCase        = val[dim : dim * 2]
                _UpperCAmelCase        = val[-dim:]
            pass
        else:
            _UpperCAmelCase        = val

    return orig_state_dict


def 			_UpperCamelCase						(    _A       ,   _A       ,   _A   )     ->	List[Any]:





    """simple docstring"""

    _UpperCAmelCase        = get_config(_A   )
    _UpperCAmelCase        = SwinaSRForImageSuperResolution(_A   )
    model.eval()

    _UpperCAmelCase        = torch.hub.load_state_dict_from_url(_A       ,   map_location="""cpu"""   )
    _UpperCAmelCase        = convert_state_dict(_A       ,   _A   )
    _UpperCAmelCase     ,_UpperCAmelCase        = model.load_state_dict(_A       ,   strict=_A   )

    if len(_A   ) > 0:
        raise ValueError("""Missing keys when converting: {}""".format(_A   )   )
    for key in unexpected_keys:
        if not ("relative_position_index" in key or "relative_coords_table" in key or "self_mask" in key):
            raise ValueError(F"""Unexpected key {key} in state_dict"""   )

    # verify values
    _UpperCAmelCase        = """https://github.com/mv-lab/swin2sr/blob/main/testsets/real-inputs/shanghai.jpg?raw=true"""
    _UpperCAmelCase        = Image.open(requests.get(_A       ,   stream=_A   ).raw   ).convert("""RGB"""   )
    _UpperCAmelCase        = SwinaSRImageProcessor()
    # pixel_values = processor(image, return_tensors="pt").pixel_values

    _UpperCAmelCase        = 1_2_6 if """Jpeg""" in checkpoint_url else 2_5_6
    _UpperCAmelCase        = Compose(
        [
            Resize((image_size, image_size)   ),
            ToTensor(),
            Normalize(mean=[0.485, 0.456, 0.406]       ,   std=[0.229, 0.224, 0.225]   ),
        ]   )
    _UpperCAmelCase        = transforms(_A   ).unsqueeze(0   )

    if config.num_channels == 1:
        _UpperCAmelCase        = pixel_values[:, 0, :, :].unsqueeze(1   )

    _UpperCAmelCase        = model(_A   )

    # assert values
    if "Swin2SR_ClassicalSR_X2_64" in checkpoint_url:
        _UpperCAmelCase        = torch.Size([1, 3, 5_1_2, 5_1_2]   )
        _UpperCAmelCase        = torch.tensor(
            [[-0.7_087, -0.7_138, -0.6_721], [-0.8_340, -0.8_095, -0.7_298], [-0.9_149, -0.8_414, -0.7_940]]   )
    elif "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
        _UpperCAmelCase        = torch.Size([1, 3, 1_0_2_4, 1_0_2_4]   )
        _UpperCAmelCase        = torch.tensor(
            [[-0.7_775, -0.8_105, -0.8_933], [-0.7_764, -0.8_356, -0.9_225], [-0.7_976, -0.8_686, -0.9_579]]   )
    elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
        # TODO values didn't match exactly here
        _UpperCAmelCase        = torch.Size([1, 3, 1_0_2_4, 1_0_2_4]   )
        _UpperCAmelCase        = torch.tensor(
            [[-0.8_035, -0.7_504, -0.7_491], [-0.8_538, -0.8_124, -0.7_782], [-0.8_804, -0.8_651, -0.8_493]]   )
    elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
        _UpperCAmelCase        = torch.Size([1, 3, 5_1_2, 5_1_2]   )
        _UpperCAmelCase        = torch.tensor(
            [[-0.7_669, -0.8_662, -0.8_767], [-0.8_810, -0.9_962, -0.9_820], [-0.9_340, -1.0_322, -1.1_149]]   )
    elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
        _UpperCAmelCase        = torch.Size([1, 3, 1_0_2_4, 1_0_2_4]   )
        _UpperCAmelCase        = torch.tensor(
            [[-0.5_238, -0.5_557, -0.6_321], [-0.6_016, -0.5_903, -0.6_391], [-0.6_244, -0.6_334, -0.6_889]]   )

    assert (
        outputs.reconstruction.shape == expected_shape
    ), F"""Shape of reconstruction should be {expected_shape}, but is {outputs.reconstruction.shape}"""
    assert torch.allclose(outputs.reconstruction[0, 0, :3, :3]       ,   _A       ,   atol=1e-3   )
    print("""Looks ok!"""   )

    _UpperCAmelCase        = {
        """https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_ClassicalSR_X2_64.pth""": (
            """swin2SR-classical-sr-x2-64"""
        ),
        """https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_ClassicalSR_X4_64.pth""": (
            """swin2SR-classical-sr-x4-64"""
        ),
        """https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_CompressedSR_X4_48.pth""": (
            """swin2SR-compressed-sr-x4-48"""
        ),
        """https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_Lightweight_X2_64.pth""": (
            """swin2SR-lightweight-x2-64"""
        ),
        """https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR.pth""": (
            """swin2SR-realworld-sr-x4-64-bsrgan-psnr"""
        ),
    }
    _UpperCAmelCase        = url_to_name[checkpoint_url]

    if pytorch_dump_folder_path is not None:
        print(F"""Saving model {model_name} to {pytorch_dump_folder_path}"""   )
        model.save_pretrained(_A   )
        print(F"""Saving image processor to {pytorch_dump_folder_path}"""   )
        processor.save_pretrained(_A   )

    if push_to_hub:
        model.push_to_hub(F"""caidas/{model_name}"""   )
        processor.push_to_hub(F"""caidas/{model_name}"""   )


if __name__ == "__main__":
 a       :						Optional[int]       		=		argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '''--checkpoint_url''',
     default='''https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_ClassicalSR_X2_64.pth''',
     type=str,
     help='''URL of the original Swin2SR checkpoint you\'d like to convert.''',
 )
 parser.add_argument(
     '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
 )
 parser.add_argument('''--push_to_hub''', action='''store_true''', help='''Whether to push the converted model to the hub.''')

 a       :						str       		=		parser.parse_args()
 convert_swinasr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)